Papers › Data-driven computing in elasticity via kernel regression
Data-driven computing in elasticity via kernel regression
Yoshihiro Kanno
This paper presents a simple nonparametric regression approach to data-driven computing in elasticity. We apply the kernel regression to the material data set, and formulate a system of nonlinear equations solved to obtain a static equilibrium state of an elastic structure. Preliminary numerical experiments illustrate that, compared with existing methods, the proposed method finds a reasonable solution even if data points distribute coarsely in a given material data set.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Stress-Strain Relation | Non-Linear Elasticity Benchmark | Kernel Regression | Time (ms) | 7.18 | #2 of 4 | Archive leaderboard | report |
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